Approximating First Hitting Point Distribution in Milestoning for Rare Event Kinetics
Ru Wang, Hao Wang, Wenjian Liu, Ron Elber

TL;DR
This paper introduces two novel algorithms, LPT-M and BI-M, to accurately approximate the first hitting point distribution in Milestoning, significantly improving the estimation of mean first passage times in rare event kinetics.
Contribution
The paper presents two new algorithms, LPT-M and BI-M, for better approximation of FHPD in Milestoning, enhancing accuracy and efficiency over classical methods.
Findings
Both methods outperform classical Milestoning in MFPT accuracy.
BI-M generalizes directional Milestoning within Hamiltonian dynamics.
LPT-M offers computational efficiency and robustness with many milestones.
Abstract
Milestoning is an efficient method for rare event kinetics calculation using short trajectory parallelization. Mean first passage time (MFPT) is the key kinetic output of Milestoning, whose accuracy crucially depends the initial distribution of the short trajectory ensemble. The true initial distribution, i.e., first hitting point distribution (FHPD), has no analytic expression in the general case. Here, we introduce two algorithms, local passage time weighted Milestoning (LPT-M) and Bayesian inference Milestoning (BI-M), to accurately and efficiently approximate FHPD for systems at equilibrium condition. Starting from sampling Boltzmann distribution on milestones, we calculate the proper weighting factor for the short trajectory ensemble. The methods are tested on two model examples for illustration purpose. Both methods improve significantly over the widely used classical Milestoning…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMarkov Chains and Monte Carlo Methods · Gaussian Processes and Bayesian Inference · Cold Atom Physics and Bose-Einstein Condensates
